Introduction

R is an interpreter >
Object-oriented <-
Case matters (i_like_snake_case)
Anything behind a # is a comment
Packages! Install once per machine, once per R session

Install once per machine

install.packages("tidyverse") # yes in quotes

Load once per R work session

library(tidyverse) # no no quotes

Overriding conflicts

select <- dplyr::select

Get help

?select

You can also get packages that are available on GitHub (may be development versions of what is on CRAN)

install.packages("devtools")
devtools::install_github("gadenbuie/ggpomological", build_vignette = TRUE)
library(ggpomological) # load like any other package

RStudio

Recommended IDE.
Download open source desktop version: https://www.rstudio.com/products/rstudio/
Use Projects! https://support.rstudio.com/hc/en-us/articles/200526207-Using-Projects

Use Projects + Git in RStudio: http://happygitwithr.com

Tidyverse

What is it? https://www.tidyverse.org

Piping/chaining with the %>% operator

With the pipe, think “data frame first, data frame once”

Problem:

Nesting your dataframe in commands is hard to read

head(iris)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

Solution:

Piping your dataframe into a command lets you read L to R

iris %>% head(.)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

Without the pipe, sequences of commands are read inside out

head(iris[iris$Species == "virginica", ])

##     Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
## 101          6.3         3.3          6.0         2.5 virginica
## 102          5.8         2.7          5.1         1.9 virginica
## 103          7.1         3.0          5.9         2.1 virginica
## 104          6.3         2.9          5.6         1.8 virginica
## 105          6.5         3.0          5.8         2.2 virginica
## 106          7.6         3.0          6.6         2.1 virginica

Chaining your commands lets you read L to R

iris %>% filter(Species == "virginica") %>% head(.)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
## 1          6.3         3.3          6.0         2.5 virginica
## 2          5.8         2.7          5.1         1.9 virginica
## 3          7.1         3.0          5.9         2.1 virginica
## 4          6.3         2.9          5.6         1.8 virginica
## 5          6.5         3.0          5.8         2.2 virginica
## 6          7.6         3.0          6.6         2.1 virginica

Reading data

Recommendation: readr package
Loaded in the tidyverse bundle.
Benefits over other base import functions:
- Faster
- Automated sensible defaults
- Sensible detection of variable types

We don’t need this as it loaded in the tidyverse package for us but a reminder…

install.packages("readr") # once per machine
library(readr) # once per work session

Read in from a url

Our data is stored in a gist: https://gist.github.com/kylebgorman/77ce12c9167554ade560af9d34565c11

I saved the raw url as a human-readable link.

mazes <- read_csv("http://bit.ly/mazes-gist")

Problem: File paths are hard

Solution: Use here::here

library(here)
mazes <- read_csv(here("data", "mazes.csv")) # from readr
glimpse(mazes) # from dplyr

## Observations: 381
## Variables: 12
## $ Study.ID <chr> "CSLU-001", "CSLU-001", "CSLU-001", "CSLU-001", "CSLU...
## $ CA       <dbl> 5.6667, 5.6667, 5.6667, 5.6667, 6.5000, 6.5000, 6.500...
## $ VIQ      <dbl> 124, 124, 124, 124, 124, 124, 124, 124, 108, 108, 108...
## $ DX       <chr> "TD", "TD", "TD", "TD", "TD", "TD", "TD", "TD", "TD",...
## $ Activity <chr> "Conversation", "Picture Description", "Play", "Wordl...
## $ Content  <dbl> 24, 1, 21, 8, 3, 5, 8, 2, 25, 10, 2, 5, 32, 20, 13, 2...
## $ Filler   <dbl> 31, 2, 6, 2, 10, 3, 8, 2, 21, 13, 10, 2, 12, 9, 4, 4,...
## $ REP      <dbl> 2, 0, 3, 0, 3, 2, 3, 0, 4, 0, 0, 0, 13, 5, 5, 6, 10, ...
## $ REV      <dbl> 5, 0, 8, 4, 0, 1, 2, 0, 4, 2, 1, 3, 8, 7, 2, 8, 5, 1,...
## $ FS       <dbl> 17, 1, 10, 4, 0, 2, 3, 2, 17, 8, 1, 2, 11, 8, 6, 7, 1...
## $ Cued     <dbl> 36, 2, 6, 2, 10, 3, 9, 2, 29, 13, 11, 2, 14, 12, 4, 1...
## $ Not.Cued <dbl> 50, 3, 27, 10, 13, 8, 15, 4, 38, 23, 11, 7, 42, 26, 1...

Problem: Variable types are hard

Solution: Use readr::read_csv(col_types)

mazes <- read_csv(here("data", "mazes.csv"), 
                  col_types = cols(
                    DX = col_factor(levels = NULL),
                    Activity = col_factor(levels = NULL)
                    )
                  ) 
glimpse(mazes) # from dplyr

## Observations: 381
## Variables: 12
## $ Study.ID <chr> "CSLU-001", "CSLU-001", "CSLU-001", "CSLU-001", "CSLU...
## $ CA       <dbl> 5.6667, 5.6667, 5.6667, 5.6667, 6.5000, 6.5000, 6.500...
## $ VIQ      <dbl> 124, 124, 124, 124, 124, 124, 124, 124, 108, 108, 108...
## $ DX       <fct> TD, TD, TD, TD, TD, TD, TD, TD, TD, TD, TD, TD, TD, T...
## $ Activity <fct> Conversation, Picture Description, Play, Wordless Pic...
## $ Content  <dbl> 24, 1, 21, 8, 3, 5, 8, 2, 25, 10, 2, 5, 32, 20, 13, 2...
## $ Filler   <dbl> 31, 2, 6, 2, 10, 3, 8, 2, 21, 13, 10, 2, 12, 9, 4, 4,...
## $ REP      <dbl> 2, 0, 3, 0, 3, 2, 3, 0, 4, 0, 0, 0, 13, 5, 5, 6, 10, ...
## $ REV      <dbl> 5, 0, 8, 4, 0, 1, 2, 0, 4, 2, 1, 3, 8, 7, 2, 8, 5, 1,...
## $ FS       <dbl> 17, 1, 10, 4, 0, 2, 3, 2, 17, 8, 1, 2, 11, 8, 6, 7, 1...
## $ Cued     <dbl> 36, 2, 6, 2, 10, 3, 9, 2, 29, 13, 11, 2, 14, 12, 4, 1...
## $ Not.Cued <dbl> 50, 3, 27, 10, 13, 8, 15, 4, 38, 23, 11, 7, 42, 26, 1...

Problem: But I’m a visual person…

Solution: Use visdat::vis_dat()

library(visdat)
vis_dat(mazes)

Problem: Variable names are messy

Solution: Use janitor::clean_names()

library(janitor)
mazes <- mazes %>% 
  clean_names()
glimpse(mazes)

## Observations: 381
## Variables: 12
## $ study_id <chr> "CSLU-001", "CSLU-001", "CSLU-001", "CSLU-001", "CSLU...
## $ ca       <dbl> 5.6667, 5.6667, 5.6667, 5.6667, 6.5000, 6.5000, 6.500...
## $ viq      <dbl> 124, 124, 124, 124, 124, 124, 124, 124, 108, 108, 108...
## $ dx       <fct> TD, TD, TD, TD, TD, TD, TD, TD, TD, TD, TD, TD, TD, T...
## $ activity <fct> Conversation, Picture Description, Play, Wordless Pic...
## $ content  <dbl> 24, 1, 21, 8, 3, 5, 8, 2, 25, 10, 2, 5, 32, 20, 13, 2...
## $ filler   <dbl> 31, 2, 6, 2, 10, 3, 8, 2, 21, 13, 10, 2, 12, 9, 4, 4,...
## $ rep      <dbl> 2, 0, 3, 0, 3, 2, 3, 0, 4, 0, 0, 0, 13, 5, 5, 6, 10, ...
## $ rev      <dbl> 5, 0, 8, 4, 0, 1, 2, 0, 4, 2, 1, 3, 8, 7, 2, 8, 5, 1,...
## $ fs       <dbl> 17, 1, 10, 4, 0, 2, 3, 2, 17, 8, 1, 2, 11, 8, 6, 7, 1...
## $ cued     <dbl> 36, 2, 6, 2, 10, 3, 9, 2, 29, 13, 11, 2, 14, 12, 4, 1...
## $ not_cued <dbl> 50, 3, 27, 10, 13, 8, 15, 4, 38, 23, 11, 7, 42, 26, 1...

Problem: We want summary statistics for all variables with one line of code

Solution: Use skimr::skim()

library(skimr)
skim(mazes)

## Skim summary statistics
##  n obs: 381 
##  n variables: 12 
## 
## Variable type: character 
##   variable missing complete   n min max empty n_unique
## 1 study_id       0      381 381   8   8     0       97
## 
## Variable type: factor 
##   variable missing complete   n n_unique
## 1 activity       0      381 381        4
## 2       dx       0      381 381        3
##                           top_counts ordered
## 1 Wor: 97, Pla: 96, Con: 94, Pic: 94   FALSE
## 2  ASD: 183, TD: 127, SLI: 71, NA: 0   FALSE
## 
## Variable type: numeric 
##   variable missing complete   n   mean    sd   min   p25 median    p75
## 1       ca       0      381 381   6.83  1.06  4.75  6.08   6.83   7.58
## 2  content       0      381 381  18.73 24.84  0     5     12     23   
## 3     cued       0      381 381  14.36 24.22  0     2      6     15   
## 4   filler       0      381 381  11.2  17.59  0     2      5     13   
## 5       fs       0      381 381   8.7  12.76  0     2      5     11   
## 6 not_cued       0      381 381  26.77 31.73  1     8     17     33   
## 7      rep       0      381 381   6.24  9.45  0     1      3      7   
## 8      rev       0      381 381   3.79  4.31  0     1      3      5   
## 9      viq       0      381 381 100.82 18.74 53    85    102    116   
##      max     hist
## 1   8.92 ▅▆▇▇▇▇▆▂
## 2 214    ▇▁▁▁▁▁▁▁
## 3 230    ▇▁▁▁▁▁▁▁
## 4 152    ▇▁▁▁▁▁▁▁
## 5 118    ▇▁▁▁▁▁▁▁
## 6 222    ▇▂▁▁▁▁▁▁
## 7  87    ▇▁▁▁▁▁▁▁
## 8  37    ▇▂▁▁▁▁▁▁
## 9 143    ▁▁▇▇▆▅▅▂

Problem: We need a sanity check for our data

Solution: Count everything

mazes %>% tally() # number of rows

## # A tibble: 1 x 1
##       n
##   <int>
## 1   381

mazes %>% distinct(study_id) %>% count() # overall n

## # A tibble: 1 x 1
##       n
##   <int>
## 1    97

mazes %>% 
  summarize(n = n_distinct(study_id))

## # A tibble: 1 x 1
##       n
##   <int>
## 1    97

mazes %>% 
  count(dx, activity) # n by group/activity

## # A tibble: 12 x 3
##    dx    activity                  n
##    <fct> <fct>                 <int>
##  1 TD    Conversation             32
##  2 TD    Picture Description      31
##  3 TD    Play                     32
##  4 TD    Wordless Picture Book    32
##  5 ASD   Conversation             45
##  6 ASD   Picture Description      45
##  7 ASD   Play                     46
##  8 ASD   Wordless Picture Book    47
##  9 SLI   Conversation             17
## 10 SLI   Picture Description      18
## 11 SLI   Play                     18
## 12 SLI   Wordless Picture Book    18

# frequency tables
mazes_tab <- mazes %>% 
  tabyl(dx, activity) # from janitor package
mazes_tab

##   dx Conversation Picture Description Play Wordless Picture Book
##   TD           32                  31   32                    32
##  ASD           45                  45   46                    47
##  SLI           17                  18   18                    18

mazes_tab %>% 
  adorn_percentages("col") %>% 
  adorn_pct_formatting() %>% 
  adorn_ns()

##   dx Conversation Picture Description       Play Wordless Picture Book
##   TD   34.0% (32)          33.0% (31) 33.3% (32)            33.0% (32)
##  ASD   47.9% (45)          47.9% (45) 47.9% (46)            48.5% (47)
##  SLI   18.1% (17)          19.1% (18) 18.8% (18)            18.6% (18)

Other packages for data import in R bundled in tidyverse:

DBI, for databases.
haven, for SPSS, SAS and Stata files.
httr, for web apis.
jsonlite for JSON.
readxl, for .xls and .xlsx files.
rvest, for web scraping.
xml2, for XML.

All together now

If you can, I suggest trying to stay in the tidyverse of packages (at least to start). Recommendations:

Use RStudio
Use Projects within RStudio
Use Git within Projects within RStudio
Use readr to import (or other tidyverse options above)
Use readr to specify column types using col_types argument
Use visdat::vis_dat() to visualize column types
Use here for file paths (hurrah!)
Use janitor::clean_names() to clean up variable names
Use skimr to skim() all the summary statistics in one line
Use dplyr to count rows (yay for sanity checks)

Taming data

Five main single-table verbs in dplyr:

select: Choose variables/columns by their names
filter: Pick rows based on conditions about their values
summarize: Create summary measures of variables (or groups of observations on variables using group_by)
mutate: Make a new variable in the data frame
arrange: Sort the rows based on one or more variables

Combine with group_by()

http://moderndive.com/5-wrangling.html

Tidying data

The package tidyr has two primary functions for tidying data:

gather()
spread()

tidyr tidy data vignette

3 rules:

1 variable = 1 column
1 observation = 1 row
1 value = 1 cell

Let’s work again with the mazes data.

It is actually already tidy. Why?

mazes

## # A tibble: 381 x 12
##    study_id    ca   viq dx    activity    content filler   rep   rev    fs
##    <chr>    <dbl> <dbl> <fct> <fct>         <dbl>  <dbl> <dbl> <dbl> <dbl>
##  1 CSLU-001  5.67   124 TD    Conversati…   24.0   31.0   2.00  5.00 17.0 
##  2 CSLU-001  5.67   124 TD    Picture De…    1.00   2.00  0     0     1.00
##  3 CSLU-001  5.67   124 TD    Play          21.0    6.00  3.00  8.00 10.0 
##  4 CSLU-001  5.67   124 TD    Wordless P…    8.00   2.00  0     4.00  4.00
##  5 CSLU-002  6.50   124 TD    Conversati…    3.00  10.0   3.00  0     0   
##  6 CSLU-002  6.50   124 TD    Picture De…    5.00   3.00  2.00  1.00  2.00
##  7 CSLU-002  6.50   124 TD    Play           8.00   8.00  3.00  2.00  3.00
##  8 CSLU-002  6.50   124 TD    Wordless P…    2.00   2.00  0     0     2.00
##  9 CSLU-007  7.50   108 TD    Conversati…   25.0   21.0   4.00  4.00 17.0 
## 10 CSLU-007  7.50   108 TD    Picture De…   10.0   13.0   0     2.00  8.00
## # ... with 371 more rows, and 2 more variables: cued <dbl>, not_cued <dbl>

glimpse(mazes)

## Observations: 381
## Variables: 12
## $ study_id <chr> "CSLU-001", "CSLU-001", "CSLU-001", "CSLU-001", "CSLU...
## $ ca       <dbl> 5.6667, 5.6667, 5.6667, 5.6667, 6.5000, 6.5000, 6.500...
## $ viq      <dbl> 124, 124, 124, 124, 124, 124, 124, 124, 108, 108, 108...
## $ dx       <fct> TD, TD, TD, TD, TD, TD, TD, TD, TD, TD, TD, TD, TD, T...
## $ activity <fct> Conversation, Picture Description, Play, Wordless Pic...
## $ content  <dbl> 24, 1, 21, 8, 3, 5, 8, 2, 25, 10, 2, 5, 32, 20, 13, 2...
## $ filler   <dbl> 31, 2, 6, 2, 10, 3, 8, 2, 21, 13, 10, 2, 12, 9, 4, 4,...
## $ rep      <dbl> 2, 0, 3, 0, 3, 2, 3, 0, 4, 0, 0, 0, 13, 5, 5, 6, 10, ...
## $ rev      <dbl> 5, 0, 8, 4, 0, 1, 2, 0, 4, 2, 1, 3, 8, 7, 2, 8, 5, 1,...
## $ fs       <dbl> 17, 1, 10, 4, 0, 2, 3, 2, 17, 8, 1, 2, 11, 8, 6, 7, 1...
## $ cued     <dbl> 36, 2, 6, 2, 10, 3, 9, 2, 29, 13, 11, 2, 14, 12, 4, 1...
## $ not_cued <dbl> 50, 3, 27, 10, 13, 8, 15, 4, 38, 23, 11, 7, 42, 26, 1...

It could have been seriously un-tidy…

## # A tibble: 2,667 x 7
##    study_id    ca   viq dx    activity            variable_type maze_count
##    <chr>    <dbl> <dbl> <fct> <fct>               <chr>              <dbl>
##  1 CSLU-001  5.67   124 TD    Conversation        content            24.0 
##  2 CSLU-001  5.67   124 TD    Picture Description content             1.00
##  3 CSLU-001  5.67   124 TD    Play                content            21.0 
##  4 CSLU-001  5.67   124 TD    Wordless Picture B… content             8.00
##  5 CSLU-002  6.50   124 TD    Conversation        content             3.00
##  6 CSLU-002  6.50   124 TD    Picture Description content             5.00
##  7 CSLU-002  6.50   124 TD    Play                content             8.00
##  8 CSLU-002  6.50   124 TD    Wordless Picture B… content             2.00
##  9 CSLU-007  7.50   108 TD    Conversation        content            25.0 
## 10 CSLU-007  7.50   108 TD    Picture Description content            10.0 
## # ... with 2,657 more rows

## Observations: 97
## Variables: 32
## $ study_id                         <chr> "CSLU-001", "CSLU-002", "CSLU...
## $ ca                               <dbl> 5.6667, 6.5000, 7.5000, 5.250...
## $ viq                              <dbl> 124, 124, 108, 112, 102, 102,...
## $ dx                               <fct> TD, TD, TD, TD, ASD, TD, ASD,...
## $ Conversation_content             <dbl> 24, 3, 25, 32, 27, 60, 30, 27...
## $ Conversation_cued                <dbl> 36, 10, 29, 14, 17, 27, 7, 24...
## $ Conversation_filler              <dbl> 31, 10, 21, 12, 12, 23, 6, 18...
## $ Conversation_fs                  <dbl> 17, 0, 17, 11, 12, 21, 9, 16,...
## $ Conversation_not_cued            <dbl> 50, 13, 38, 42, 34, 79, 35, 3...
## $ Conversation_rep                 <dbl> 2, 3, 4, 13, 10, 20, 17, 4, 5...
## $ Conversation_rev                 <dbl> 5, 0, 4, 8, 5, 19, 4, 7, 1, 1...
## $ `Picture Description_content`    <dbl> 1, 5, 10, 20, 9, 36, 21, 4, N...
## $ `Picture Description_cued`       <dbl> 2, 3, 13, 12, 7, 11, 2, 5, NA...
## $ `Picture Description_filler`     <dbl> 2, 3, 13, 9, 6, 6, 2, 5, NA, ...
## $ `Picture Description_fs`         <dbl> 1, 2, 8, 8, 3, 10, 11, 1, NA,...
## $ `Picture Description_not_cued`   <dbl> 3, 8, 23, 26, 14, 37, 23, 9, ...
## $ `Picture Description_rep`        <dbl> 0, 2, 0, 5, 5, 16, 8, 1, NA, ...
## $ `Picture Description_rev`        <dbl> 0, 1, 2, 7, 1, 10, 2, 2, NA, ...
## $ Play_content                     <dbl> 21, 8, 2, 13, 12, 17, 54, 10,...
## $ Play_cued                        <dbl> 6, 9, 11, 4, 9, 5, 7, 12, 2, ...
## $ Play_filler                      <dbl> 6, 8, 10, 4, 7, 4, 5, 9, 2, 2...
## $ Play_fs                          <dbl> 10, 3, 1, 6, 3, 7, 17, 4, 3, ...
## $ Play_not_cued                    <dbl> 27, 15, 11, 17, 17, 20, 57, 1...
## $ Play_rep                         <dbl> 3, 3, 0, 5, 5, 3, 26, 1, 5, 4...
## $ Play_rev                         <dbl> 8, 2, 1, 2, 4, 7, 11, 5, 0, 5...
## $ `Wordless Picture Book_content`  <dbl> 8, 2, 5, 21, 6, 14, 17, 8, 4,...
## $ `Wordless Picture Book_cued`     <dbl> 2, 2, 2, 11, 4, 10, 0, 0, 3, ...
## $ `Wordless Picture Book_filler`   <dbl> 2, 2, 2, 4, 3, 6, 0, 0, 2, 5,...
## $ `Wordless Picture Book_fs`       <dbl> 4, 2, 2, 7, 2, 9, 6, 4, 0, 15...
## $ `Wordless Picture Book_not_cued` <dbl> 10, 4, 7, 18, 8, 16, 17, 8, 5...
## $ `Wordless Picture Book_rep`      <dbl> 0, 0, 0, 6, 2, 3, 6, 2, 3, 4,...
## $ `Wordless Picture Book_rev`      <dbl> 4, 0, 3, 8, 2, 2, 5, 2, 1, 4,...

How could we have fixed this?

mazes_step1 <- mazes_widest %>% 
  gather(variable_name, maze_count, -c(study_id, ca, viq, dx))
mazes_step1

## # A tibble: 2,716 x 6
##    study_id    ca   viq dx    variable_name        maze_count
##    <chr>    <dbl> <dbl> <fct> <chr>                     <dbl>
##  1 CSLU-001  5.67 124   TD    Conversation_content      24.0 
##  2 CSLU-002  6.50 124   TD    Conversation_content       3.00
##  3 CSLU-007  7.50 108   TD    Conversation_content      25.0 
##  4 CSLU-010  5.25 112   TD    Conversation_content      32.0 
##  5 CSLU-020  8.25 102   ASD   Conversation_content      27.0 
##  6 CSLU-024  6.67 102   TD    Conversation_content      60.0 
##  7 CSLU-027  8.17  81.0 ASD   Conversation_content      30.0 
##  8 CSLU-031  5.67 143   TD    Conversation_content      27.0 
##  9 CSLU-036  6.17  77.0 ASD   Conversation_content       7.00
## 10 CSLU-046  5.25 129   ASD   Conversation_content      36.0 
## # ... with 2,706 more rows

mazes_step2 <- mazes_step1 %>% 
  separate(variable_name, into = c("activity", "var_type"), sep = "_") 
mazes_step2

## # A tibble: 2,716 x 7
##    study_id    ca   viq dx    activity     var_type maze_count
##    <chr>    <dbl> <dbl> <fct> <chr>        <chr>         <dbl>
##  1 CSLU-001  5.67 124   TD    Conversation content       24.0 
##  2 CSLU-002  6.50 124   TD    Conversation content        3.00
##  3 CSLU-007  7.50 108   TD    Conversation content       25.0 
##  4 CSLU-010  5.25 112   TD    Conversation content       32.0 
##  5 CSLU-020  8.25 102   ASD   Conversation content       27.0 
##  6 CSLU-024  6.67 102   TD    Conversation content       60.0 
##  7 CSLU-027  8.17  81.0 ASD   Conversation content       30.0 
##  8 CSLU-031  5.67 143   TD    Conversation content       27.0 
##  9 CSLU-036  6.17  77.0 ASD   Conversation content        7.00
## 10 CSLU-046  5.25 129   ASD   Conversation content       36.0 
## # ... with 2,706 more rows

mazes_step3 <- mazes_step2 %>% 
  spread(var_type, maze_count)
mazes_step3

## # A tibble: 388 x 12
##    study_id    ca   viq dx    activity    content  cued filler    fs   not
##    <chr>    <dbl> <dbl> <fct> <chr>         <dbl> <dbl>  <dbl> <dbl> <dbl>
##  1 CSLU-001  5.67   124 TD    Conversati…   24.0  36.0   31.0  17.0  50.0 
##  2 CSLU-001  5.67   124 TD    Picture De…    1.00  2.00   2.00  1.00  3.00
##  3 CSLU-001  5.67   124 TD    Play          21.0   6.00   6.00 10.0  27.0 
##  4 CSLU-001  5.67   124 TD    Wordless P…    8.00  2.00   2.00  4.00 10.0 
##  5 CSLU-002  6.50   124 TD    Conversati…    3.00 10.0   10.0   0    13.0 
##  6 CSLU-002  6.50   124 TD    Picture De…    5.00  3.00   3.00  2.00  8.00
##  7 CSLU-002  6.50   124 TD    Play           8.00  9.00   8.00  3.00 15.0 
##  8 CSLU-002  6.50   124 TD    Wordless P…    2.00  2.00   2.00  2.00  4.00
##  9 CSLU-007  7.50   108 TD    Conversati…   25.0  29.0   21.0  17.0  38.0 
## 10 CSLU-007  7.50   108 TD    Picture De…   10.0  13.0   13.0   8.00 23.0 
## # ... with 378 more rows, and 2 more variables: rep <dbl>, rev <dbl>

Let’s do another one…

Visualizing data

Scatterplots

(plot <- ggplot(mazes, aes(x = content, y = filler)) +
  geom_point())

(plot +
  facet_wrap(~activity))

plot <- ggplot(mazes, aes(x = content, y = filler)) +
  geom_point(alpha = .3) +
  facet_wrap(~activity)
plot

plot <- ggplot(mazes, aes(x = content, y = filler, color = dx)) +
  geom_point(alpha = .5) +
  facet_wrap(~activity)
plot

plot <- ggplot(mazes, aes(x = content, y = filler)) +
  geom_smooth(se = FALSE) +
  geom_point(aes(color = content), shape = 10) +
  facet_wrap(~activity) 
  
plot

Challenge!

Try to change the lines to use the linear model (lm) as the smoothing method (function) to use.
Remove color from the global aesthetics and add geom_point(aes(color = dx), alpha = .5). What changes?

Distributions

ggplot(mazes, aes(x = activity, y = content)) +
  geom_boxplot()

ggplot(mazes, aes(x = activity, y = content)) +
  geom_violin()

ggplot(mazes, aes(x = activity, y = content)) +
  geom_violin(fill = "lightgray", color = NA) +
  geom_boxplot(width = .1, colour = "slateblue", fill = "black")

ggplot(mazes, aes(content)) +
  geom_histogram() +
  facet_wrap(~activity)

ggplot(mazes, aes(content, fill = dx)) +
  geom_density(alpha = .5) +
  facet_wrap(~activity)

Working with Data in R

Alison Hill